





'''simple docstring'''


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase   :	List[str]   				={
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowerCAmelCase   :	Dict   				={
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowerCAmelCase   :	Optional[Any]   				={
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowerCAmelCase   :	Union[str, Any]   				={
    """num_train_timesteps""": 40,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCAmelCase   :	Tuple   				={
    """num_train_timesteps""": 201,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

lowerCAmelCase   :	Any   				={
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Any					):



   if isinstance(a__      ,a__					):
      return v
   if v.lower() in ("yes", "true", "t", "y", "1"):
      return True
   elif v.lower() in ("no", "false", "f", "n", "0"):
      return False
   else:
      raise argparse.ArgumentTypeError("boolean value expected"					)
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Any      ,__lowerCamelCase   :   Tuple      ,__lowerCamelCase   :   List[Any]      ,__lowerCamelCase   :   Optional[int]      ,__lowerCamelCase   :   Optional[Any]=False					):
   lowercase_     :Dict            =					checkpoint[F'{old_prefix}.in_layers.0.weight']
   lowercase_     :Dict            =					checkpoint[F'{old_prefix}.in_layers.0.bias']
   lowercase_     :List[Any]            =					checkpoint[F'{old_prefix}.in_layers.2.weight']
   lowercase_     :List[str]            =					checkpoint[F'{old_prefix}.in_layers.2.bias']
   lowercase_     :Tuple            =					checkpoint[F'{old_prefix}.emb_layers.1.weight']
   lowercase_     :int            =					checkpoint[F'{old_prefix}.emb_layers.1.bias']
   lowercase_     :str            =					checkpoint[F'{old_prefix}.out_layers.0.weight']
   lowercase_     :Optional[int]            =					checkpoint[F'{old_prefix}.out_layers.0.bias']
   lowercase_     :Any            =					checkpoint[F'{old_prefix}.out_layers.3.weight']
   lowercase_     :Dict            =					checkpoint[F'{old_prefix}.out_layers.3.bias']

   if has_skip:
      lowercase_     :Optional[int]            =					checkpoint[F'{old_prefix}.skip_connection.weight']
      lowercase_     :Dict            =					checkpoint[F'{old_prefix}.skip_connection.bias']

   return new_checkpoint
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Union[str, Any]      ,__lowerCamelCase   :   Tuple      ,__lowerCamelCase   :   Tuple=None					):
   lowercase_     :str            =					checkpoint[F'{old_prefix}.qkv.weight'].chunk(3      ,dim=0					)
   lowercase_     :Union[str, Any]            =					checkpoint[F'{old_prefix}.qkv.bias'].chunk(3      ,dim=0					)

   lowercase_     :List[str]            =					checkpoint[F'{old_prefix}.norm.weight']
   lowercase_     :Any            =					checkpoint[F'{old_prefix}.norm.bias']

   lowercase_     :List[Any]            =					weight_q.squeeze(-1					).squeeze(-1					)
   lowercase_     :Optional[Any]            =					bias_q.squeeze(-1					).squeeze(-1					)
   lowercase_     :List[Any]            =					weight_k.squeeze(-1					).squeeze(-1					)
   lowercase_     :List[str]            =					bias_k.squeeze(-1					).squeeze(-1					)
   lowercase_     :Tuple            =					weight_v.squeeze(-1					).squeeze(-1					)
   lowercase_     :List[Any]            =					bias_v.squeeze(-1					).squeeze(-1					)

   lowercase_     :str            =					(
       checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1					).squeeze(-1					)
   )
   lowercase_     :Optional[int]            =					checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1					).squeeze(-1					)

   return new_checkpoint
def 				UpperCAmelCase_    (		__lowerCamelCase   :   Optional[Any]      ,__lowerCamelCase   :   Dict					):
   lowercase_     :int            =					torch.load(a__      ,map_location="cpu"					)
   lowercase_     :Optional[Any]            =					{}

   lowercase_     :Tuple            =					checkpoint["""time_embed.0.weight"""]
   lowercase_     :List[Any]            =					checkpoint["""time_embed.0.bias"""]
   lowercase_     :Optional[Any]            =					checkpoint["""time_embed.2.weight"""]
   lowercase_     :List[Any]            =					checkpoint["""time_embed.2.bias"""]

   if unet_config["num_class_embeds"] is not None:
      lowercase_     :Dict            =					checkpoint["""label_emb.weight"""]

   lowercase_     :Dict            =					checkpoint["""input_blocks.0.0.weight"""]
   lowercase_     :Optional[int]            =					checkpoint["""input_blocks.0.0.bias"""]

   lowercase_     :int            =					unet_config["""down_block_types"""]
   lowercase_     :Dict            =					unet_config["""layers_per_block"""]
   lowercase_     :Optional[Any]            =					unet_config["""attention_head_dim"""]
   lowercase_     :Any            =					unet_config["""block_out_channels"""]
   lowercase_     :int            =					1
   lowercase_     :str            =					channels_list[0]

   for i, layer_type in enumerate(a__					):
      lowercase_     :Optional[Any]            =					channels_list[i]
      lowercase_     :Any            =					current_channels != prev_channels
      if layer_type == "ResnetDownsampleBlock2D":
         for j in range(a__					):
            lowercase_     :Optional[int]            =					F'down_blocks.{i}.resnets.{j}'
            lowercase_     :List[str]            =					F'input_blocks.{current_layer}.0'
            lowercase_     :List[Any]            =					True if j == 0 and downsample_block_has_skip else False
            lowercase_     :Optional[Any]            =					convert_resnet(a__      ,a__      ,a__      ,a__      ,has_skip=a__					)
            current_layer += 1

      elif layer_type == "AttnDownBlock2D":
         for j in range(a__					):
            lowercase_     :List[str]            =					F'down_blocks.{i}.resnets.{j}'
            lowercase_     :Dict            =					F'input_blocks.{current_layer}.0'
            lowercase_     :int            =					True if j == 0 and downsample_block_has_skip else False
            lowercase_     :Any            =					convert_resnet(a__      ,a__      ,a__      ,a__      ,has_skip=a__					)
            lowercase_     :Optional[int]            =					F'down_blocks.{i}.attentions.{j}'
            lowercase_     :int            =					F'input_blocks.{current_layer}.1'
            lowercase_     :Dict            =					convert_attention(
                a__      ,a__      ,a__      ,a__      ,a__					)
            current_layer += 1

      if i != len(a__					) - 1:
         lowercase_     :str            =					F'down_blocks.{i}.downsamplers.0'
         lowercase_     :Any            =					F'input_blocks.{current_layer}.0'
         lowercase_     :List[str]            =					convert_resnet(a__      ,a__      ,a__      ,a__					)
         current_layer += 1

      lowercase_     :int            =					current_channels

   # hardcoded the mid-block for now
   lowercase_     :List[Any]            =					"""mid_block.resnets.0"""
   lowercase_     :List[str]            =					"""middle_block.0"""
   lowercase_     :Tuple            =					convert_resnet(a__      ,a__      ,a__      ,a__					)
   lowercase_     :Dict            =					"""mid_block.attentions.0"""
   lowercase_     :str            =					"""middle_block.1"""
   lowercase_     :Union[str, Any]            =					convert_attention(a__      ,a__      ,a__      ,a__      ,a__					)
   lowercase_     :Tuple            =					"""mid_block.resnets.1"""
   lowercase_     :Dict            =					"""middle_block.2"""
   lowercase_     :List[str]            =					convert_resnet(a__      ,a__      ,a__      ,a__					)

   lowercase_     :Union[str, Any]            =					0
   lowercase_     :Tuple            =					unet_config["""up_block_types"""]

   for i, layer_type in enumerate(a__					):
      if layer_type == "ResnetUpsampleBlock2D":
         for j in range(layers_per_block + 1					):
            lowercase_     :Optional[Any]            =					F'up_blocks.{i}.resnets.{j}'
            lowercase_     :List[Any]            =					F'output_blocks.{current_layer}.0'
            lowercase_     :List[Any]            =					convert_resnet(a__      ,a__      ,a__      ,a__      ,has_skip=a__					)
            current_layer += 1

         if i != len(a__					) - 1:
            lowercase_     :Optional[Any]            =					F'up_blocks.{i}.upsamplers.0'
            lowercase_     :Tuple            =					F'output_blocks.{current_layer-1}.1'
            lowercase_     :Tuple            =					convert_resnet(a__      ,a__      ,a__      ,a__					)
      elif layer_type == "AttnUpBlock2D":
         for j in range(layers_per_block + 1					):
            lowercase_     :Optional[int]            =					F'up_blocks.{i}.resnets.{j}'
            lowercase_     :List[Any]            =					F'output_blocks.{current_layer}.0'
            lowercase_     :Any            =					convert_resnet(a__      ,a__      ,a__      ,a__      ,has_skip=a__					)
            lowercase_     :Any            =					F'up_blocks.{i}.attentions.{j}'
            lowercase_     :List[str]            =					F'output_blocks.{current_layer}.1'
            lowercase_     :List[str]            =					convert_attention(
                a__      ,a__      ,a__      ,a__      ,a__					)
            current_layer += 1

         if i != len(a__					) - 1:
            lowercase_     :str            =					F'up_blocks.{i}.upsamplers.0'
            lowercase_     :Optional[int]            =					F'output_blocks.{current_layer-1}.2'
            lowercase_     :Union[str, Any]            =					convert_resnet(a__      ,a__      ,a__      ,a__					)

   lowercase_     :Tuple            =					checkpoint["""out.0.weight"""]
   lowercase_     :int            =					checkpoint["""out.0.bias"""]
   lowercase_     :str            =					checkpoint["""out.2.weight"""]
   lowercase_     :List[Any]            =					checkpoint["""out.2.bias"""]

   return new_checkpoint


if __name__ == "__main__":
 lowerCAmelCase   :	List[Any]   				=argparse.ArgumentParser()

 parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
 parser.add_argument(
     '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
 )
 parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

 lowerCAmelCase   :	List[str]   				=parser.parse_args()
 lowerCAmelCase   :	List[Any]   				=strabool(args.class_cond)

 lowerCAmelCase   :	List[Any]   				=os.path.basename(args.unet_path)
 print(F'''Checkpoint: {ckpt_name}''')

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  lowerCAmelCase   :	str   				=IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  lowerCAmelCase   :	Union[str, Any]   				=LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  lowerCAmelCase   :	List[str]   				=TEST_UNET_CONFIG
 else:
  raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

 if not args.class_cond:
  lowerCAmelCase   :	List[Any]   				=None

 lowerCAmelCase   :	str   				=con_pt_to_diffuser(args.unet_path, unet_config)

 lowerCAmelCase   :	Any   				=UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  lowerCAmelCase   :	List[str]   				=CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  lowerCAmelCase   :	str   				=CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  lowerCAmelCase   :	Optional[int]   				=CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

 lowerCAmelCase   :	Tuple   				=CMStochasticIterativeScheduler(**scheduler_config)

 lowerCAmelCase   :	Tuple   				=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


